Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

Park, Soonyong; Kim, Soohwan; Park, Mignon; Park, Sung-Kee

doi:10.1016/j.ins.2009.06.030

Cited by 31 publications

(20 citation statements)

References 37 publications

(41 reference statements)

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“…While mentioned researchers aimed to derive semantic concept from the functionality of the objects into the map, some others such as [8], [12] and [7], introduced properties of the regions as semantic label. [8] annotates an occupancy map with the properties of the regions, either "building" or "nature", through data from range scanner and vision.…”

Section: Related Workmentioning

confidence: 99%

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Modeling of a Large Structured Environment

Shahbandi

Åstrand

2014

Advances in Autonomous Robotics Systems

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Abstract. AIMS project attempts to link the logistic requirements of an intelligent warehouse and state of the art core technologies of automation, by providing an awareness of the environment to the autonomous systems and vice versa. In this work we investigate a solution for modeling the infrastructure of a structured environment such as warehouses, by the means of a vision sensor. The model is based on the expected pattern of the infrastructure, generated from and matched to the map. Generation of the model is based on a set of tools such as closed-form Hough transform, DBSCAN clustering algorithm, Fourier transform and optimization techniques. The performance evaluation of the proposed method is accompanied with a real world experiment.

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Section: Related Workmentioning

confidence: 99%

“…In [12] properties of the environment such as terrain map and activity map, are embedded into a metric occupancy map. Concerning the global localization, [7] employed hybrid geometric-object map.…”

Section: Related Workmentioning

confidence: 99%

Modeling of a Large Structured Environment

Shahbandi

Åstrand

2014

Advances in Autonomous Robotics Systems

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“…Over the last years, great efforts have been made on the study and research of the EKF-based SLAM methods sustained by visual sensors [7,1,8,2,9,10]. The main efforts have been concentrated on the position estimation of a 3D visual landmarks set in a common reference system.…”

Section: Introductionmentioning

confidence: 99%

Information-based view initialization in visual SLAM with a single omnidirectional camera

Valiente

Ghaffari

Miró

et al. 2015

Robotics and Autonomous Systems

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This paper presents a novel mechanism to initiate new views within the map building process for an EKF-based visual SLAM (Simultaneous Localization and Mapping) approach using omnidirectional images. In presence of non-linearities, the EKF is very likely to compromise the final estimation. Particularly, the omnidirectional observation model is induces non-linear errors, thus it becomes a potential source of uncertainty. To deal with this issue we propose a novel mechanism for view initialization which accounts for information gain and losses more efficiently. The main outcome of this contribution is the reduction of the map uncertainty and thus the higher consistency of the final estimation. Its basis relies on a Gaussian Process to infer an information distribution model from sensor data. This model represents feature points existence probabilities and their information content analysis leads to the proposed view initialization scheme. To demonstrate the suitability and effectiveness of the approach we present a series of real data experiments conducted with a robot equipped with a camera sensor and map model solely based on omnidirectional views. The results reveal a beneficial reduction on the uncertainty but also on the error in the pose and the map estimate.

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“…Simultaneous Localization and Mapping (SLAM) techniques [13], [9] have been proposed to overcome this issue, which can be thought of as a chicken-and-egg problem: an unbiased map is necessary for localization, while an accurate pose estimate is needed to build that map. On the one hand, SLAM can be considered as a global rectification [34] of the mobile robot's poses along its route once the loop is closed, and when the robot observes again a previously seen object. On the other hand, estimating the movement performed by a mobile robot using the observations gathered between two consecutive poses is considered to give a local rectification.…”

Section: Introductionmentioning

confidence: 99%

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

Viejo

García-Rodríguez

Cazorla

2014

Information Sciences

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The use of 3D data in mobile robotics provides valuable information about the robot's environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique.The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

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Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

Cited by 31 publications

References 37 publications

Modeling of a Large Structured Environment

Modeling of a Large Structured Environment

Information-based view initialization in visual SLAM with a single omnidirectional camera

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

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